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That’s the title of a new article, now online at the Journal of Quantitative Criminology. Thanks to fellow grad students Cassy Dorff and Shahryar Minhas for their feedback. Thanks also to mentors at the University of Houston (Jim Granato, Ryan Kennedy) and Duke University (Michael D. Ward, Scott de Marchi, Guillermo Trejo) for thoughtful comments. The anonymous reviewers at JQC and elsewhere were also a big help.

Here is the abstract:

Objectives

Has the Mexican government’s policy of removing drug-trafficking organization (DTO) leaders reduced or increased violence? In the first 4 years of the Calderón administration, over 34,000 drug-related murders were committed. In response, the Mexican government captured or killed 25 DTO leaders. This study analyzes changes in violence (drug-related murders) that followed those leadership removals.

Methods

The analysis consists of cross-sectional time-series negative binomial modeling of 49 months of murder counts in 32 Mexican states (including the federal district).

Results

Leadership removals are generally followed by increases in drug-related murders. A DTO’s home state experiences more subsequent violence than the state where the leader was removed. Killing leaders is associated with more violence than capturing them. However, removing leaders for whom a $30m peso bounty was offered is associated with a smaller increase than other removals.

Conclusion

DTO leadership removals in Mexico were associated with an estimated 415 additional deaths during the first 4 years of the Calderón administration. Reforming Mexican law enforcement and improving career prospects for young men are more promising counter-narcotics strategies. Further research is needed to analyze how the rank of leaders mediates the effect of their removal.

I didn’t shell out $3,000 for open access, so the article is behind a paywall. If you’d like a draft of the manuscript just email me.

As you probably know by now, the Sinaloa cartel’s leader Joaquin Guzmán was captured in Mexico last Saturday. How will violence in Mexico shift following Guzman’s removal?

(Alfredo Estrella/AFP/Getty Images)

I take up this question in an article forthcoming in the Journal of Quantitative Criminology. According to that research (which used negative binomial modeling on a cross-sectional time series of Mexican states from 2006 to 2010), DTO leadership removals in Mexico are generally followed by increased violence. However, capturing leaders is associated with less violence than killing them. The removal of leaders for whom a 30 million peso bounty (the highest in my dataset, which generally identified high-level leaders) been offered is also associated with less violence. The reward for Guzmán’s capture was higher than any other contemporary DTO leader: 87 million pesos. Given that Guzmán was a top-level leader and was arrested rather than killed, I would not expect a significant uptick in violence (in the next 6 months) due to his removal. This follows President Pena Nieto’s goal of reducing DTO violence.

My paper was in progress for a while, so the data is a few years old. Fortunately Brian Phillips has also taken up this question using additional data and similar methods, and his results largely corroborate mine:

Many governments kill or capture leaders of violent groups, but research on consequences of this strategy shows mixed results. Additionally, most studies have focused on political groups such as terrorists, ignoring criminal organizations – even though they can represent serious threats to security. This paper presents an argument for how criminal groups differ from political groups, and uses the framework to explain how decapitation should affect criminal groups in particular. Decapitation should weaken organizations, producing a short-term decrease in violence in the target’s territory. However, as groups fragment and newer groups emerge to address market demands, violence is likely to increase in the longer term. Hypotheses are tested with original data on Mexican drug-trafficking organizations (DTOs), 2006-2012, and results generally support the argument. The kingpin strategy is associated with a reduction of violence in the short term, but an increase in violence in the longer term. The reduction in violence is only associated with leaders arrested, not those killed.

As Joe Blitzstein likes to say, “Thinking conditionally is a condition for thinking.” Humans are not naturally good at this skill. Consider the following example: Kelly is interested in books and keeping things organized. She loves telling stories and attending book clubs. Is it more likely that Kelly is a bestselling novelist or an accountant?

Many of the “facts” about Kelly in that story might lead you to answer that she is a novelist. Only one–her sense of organization–might have pointed you toward an accountant. But think about the overall probability of each career. Very few bookworms become successful novelists, and there are many more accountants than (successful) authors in the modern workforce. Conditioning on the baseline rate helps make a more accurate decision.

One piece of advice that Good Judgment forecasters are often reminded of is to use the baseline rate of an event as a starting point for their forecast. For example, insurgencies are a very rare event on the whole. For the period January, 2001 to August, 2013, insurgencies occurred in less than 10 percent of country-months in the ICEWS data set.…

From this baseline, we can then incorporate information about the specific countries at hand and their recent history… Mozambique has not experienced an insurgency for the entire period of the ICEWS dataset. On the other hand, Chad had an insurgency that ended in December, 2003, and another that extended from November, 2005, to April, 2010. For the duration of the ICEWS data set, Chad has experienced an insurgency 59 percent of the time. This suggests that our predicted probability of insurgency in Chad should be higher than for Mozambique.

I started writing that post before rebels in Mozambique broke their treaty with the government. Maybe I spoke too soon, but the larger point is that baselines are the starting point–not the final product–of any successful forecast.

Having more data is useful, as long as it contributes more signal than noise. That’s what ICEWS aims to do, and I consider it a useful addition to the toolbox of forecasters participating in the Good Judgment Project. For more on this collaboration, as well as a map of insurgency rates around the globe as measured by ICEWS, see the aforementioned post here.

Like this:

The lab of Michael D. Ward et al now has a blog. The inaugural post describes some of the lab’s ongoing projects that may come up in future entries including modeling of protests, insurgencies, and rebellions, event prediction (such as IED explosions), and machine learning techniques.

The second post compares two event data sets–GDELT and ICEWS–using recent political unrest in the Middle East as a focal point (more here):

We looked at protest events in Egypt and Turkey in 2011 and 2012 for both data sets, and we also looked at fighting in Syria over the same period…. What did we learn from these, limited comparisons? First, we found out first hand what the GDELT community has been saying: the GDELT data are in BETA and currently have a lot of false positives. This is not optimal for a decision making aid such as ICEWS, in which drill-down to the specific events resulting in new predictions is a requirement. Second, no one has a good ground truth for event data — though we have some ideas on this and are working on a study to implement them. Third, geolocation is a boon. GDELT seems especially good a this, even with a lot of false positives.

The visualization, which I worked on as part of the lab, can be found here. It relies on CartoDB to serve data from GDELT and ICEWS, with some preprocessing done using MySQL and R. The front-end is Javascript using a combination of d3 for timelines and Torque for maps.

GDELT (green) and ICEWS (blue) records of protests in Egypt and Turkey and conflict in Syria

If you have questions about the visualizations or the technology behind them, feel free to mention them here or on the lab blog.

We have briefly mentioned Morten Jerven’s work Poor Numbersbefore, but it deserves a bit more attention. The book discusses the woeful state of GDP figures in Africa and the issues that arise in making cross-national comparisons between countries whose statistical offices operate very differently (interview here).

Discussing Jerven’s work now is especially timely given current events. Jerven was scheduled to speak at UNECA on statistical capacity in Africa. However, Pali Lehohla of South Africa strongly objected to Jerven’s ideas and led the opposition which ultimately prevented Jerven from speaking. Had he been allowed to present, Jerven’s speech would have summarized the issues thusly:

I would argue that ambitions should be tempered in international development statistics. The international standardization of measurement of economic development has led to a procedural bias. There has been a tendency to aim for high adherence to procedures instead of focusing on the content of the measures. Development measures should be taken as a starting point in local data availability, and statisticians should refrain from reporting aggregate measures that appear to be based on data but in fact are very feeble projections or guesses. This means that it is necessary to shift the focus away from formulas, standards, handbooks, and software. What matters are what numbers are available and how good those numbers are. Comparability across time and space needs to start with the basic input of knowledge, not with the system in which this information is organized. (Jerven, 2013, p.107).

The initial response from many economists working on Africa varied between, ‘so what?…we already know this’, ‘we don’t trust or use official statistics on Africa anyhow’ and ‘I know but what is the alternative?’ Many more scholars in African studies and development studies, who were generally concerned with the long-standing use of numbers on Africa as ‘facts’, were relieved that there was finally someone who sought, not only to unveil the real state of affairs, but genuinely wanted to answer some of the problems that users face when trying to use the data to test their scholarly questions….

We need to rethink the demand for data and how we invest in data in Africa and beyond. My focus has been on Africa because the problem is particularly striking there. To fix the gaps we should first re-think the MDG and other donor agendas for data and do a cost benefit analysis – what are the costs of providing these data and what is the opportunity cost of providing these data? The opportunity cost is often ignored. Local demand for data needs to come into focus. A statistical office is only sustainable if it serves local needs for information. Statistics is a public good, and we need a good open debate on how to supply them.

This is a major issue, and all social scientists–not just economists–should be aware of Jerven’s work. As James C. Scott has pointed out, measurement is a political act.

I have been arguing for years that organized crime has an inherently political component. Certainly I am not alone, and researchers far superior to me have made the same point–for example, Charles Tilly and James Buchanan. However, mainstream political reporting seems to have been catching onto this over the past few months. I have rounded up a few of these posts that will be of interest to long-time readers. See also my working paper on violence following targeted leadership removals in Mexico.

Peter Andreas responds to Moisés Naim’s essay in “Measuring the Mafia-State Menace.” I was not aware of Andreas’s work until Daniel Solomon recently shared it on Twitter but now I have several of his books (including this one) on my reading list.

Although somewhat sensationalized, Christian Caryl also has a nice overview piece on global organized crime at Foreign Policy: “Mob Rule.” Some of the statistics there seem questionable but the overall point–that students of politics should pay attention to organized crime–is a valid and important one.

Finally, World Politics Review features an interview with Brian Phillips, who argues that targeting DTO leaders in Mexico has not reduced violence. This matches my own research on the topic.

I cannot speak to the accuracy of this since I do not know much about the internal workings of Microsoft, but as an analogy I found it fascinating. David Auerbach on how The Wire explains Microsoft:

What does Microsoft in the Ballmer era have in common with drug kingpin Avon Barksdale’s organization in The Wire? For years, both of them had the strongest package. They owned their territory, owned their market, owned their users. They were untouchable. Then times changed, bringing new competitors with new, intense products. Their own product went weak. But they couldn’t let go. “We got a weak product, and we holding on to prime real estate with no muscle,” Avon’s cerebral second-in-command, Stringer Bell, complains to him. For the Barksdale organization, the product was heroin and the real estate was the drug-ravaged Franklin Towers housing project. For Microsoft, the product is Windows and the real estate is the PC.

Like this:

Traders across Syria reported widely fluctuating rates and two currency dealers in Damascus, where the pound appeared to be hit hardest, said it fell below 200 to the dollar for the first time in what one described as panic buying of the U.S. currency.

On Monday evening the pound traded at 205 to the dollar, down 20 percent in four days and 77 percent down since the start of the anti-Assad uprising in March 2011 when it was at 47.

The idea of examining currency prices over the course of a conflict is interesting. There are a number of confounders of course. For instance, the regime can often intervene in certain ways to affect the value of currency. Other incidents besides the conflict itself can also drive currency fluctuations, especially when the conflict is relatively minor.

One nice case (from strictly a research perspective) is the US Civil War, when both the Union and Confederacy issued their own notes. Jeffrey Arnold‘s project, “Pricing the Costly Lottery: Financial Market Reactions to Battleﬁeld Events in the American Civil War,” leverages this fact to see how markets responded to successes and failures of either side. We discussed this project before when it was presented as a poster at PolMeth 2012, and Jeffrey’s website now has his MPSA 2013 slides.

Here’s his abstract, and one of my favorite graphs:

What role does combat play in resolving the disagreement that initiated war? Bargaining theories of war propose two mechanisms, the destruction of capabilities and the revelation of private information. These mechanisms are difficult to analyze quantitatively because the mechanisms are observationally equivalent, the participants’ expectations are unobservable, and there is a lack of data on battles. With new methods and new data on the American Civil War, I address these challenges. I estimate the information revealed by combat with a model of Bayesian learning. I use prices of Union and Cnnnonfederate currencies to measure public expectations of war duration and outcome. Data on battlefield events come from detailed data on the outcomes and casualties of the battles of the American Civil War. The results suggest that battle outcomes rather than casualties or information revelation had the largest influence on the expected duration of the American Civil War.

How many people died because of the September 11 attacks? The answer depends on what you are trying to measure. The official estimate is around 3,000 deaths as a direct result of hijacked aircraft and at the World Trade Center, Pentagon, and in Pennsylvania. Those attacks were tragic, but the effect was compounded by overreaction to terrorism. Specifically, enough Americans substituted driving for flying in the remaining months of 2001 to cause 350 additional deaths from accidents.

David Myers was the first to raise this possibility in a December, 2001, essay. In 2004, Gerd Gigerenzer collected data and estimated the 350 deaths figure, resulting from what he called “dread risk”:

People tend to fear dread risks, that is, low-probability, high-consequence events, such as the terrorist attack on September 11, 2001. If Americans avoided the dread risk of flying after the attack and instead drove some of the unflown miles, one would expect an increase in traffic fatalities. This hypothesis was tested by analyzing data from the U.S. Department of Transportation for the 3 months following September 11. The analysis suggests that the number of Americans who lost their lives on the road by avoiding the risk of flying was higher than the total number of passengers killed on the four fatal flights. I conclude that informing the public about psychological research concerning dread risks could possibly save lives.

Does the same effect carry over to other countries and attacks? Alejandro López-Rousseau looked at how Spaniards responded to the March 11, 2004, train bombings in Madrid. He found that activity across all forms of transportation decreased–travelers did not substitute driving for riding the train.

What could explain these differences? One could be that Americans are less willing to forego travel than Spaniards. Perhaps more travel is for business reasons and cannot be delayed. Another possibility is that Spanish citizens are more accustomed to terrorist attacks and understand that substituting driving is more risky than continuing to take the train. There are many other differences that we have not considered here–the magnitude of the two attacks, feelings of being “in control” while driving, varying cultural attitudes.

This post is simply meant to make three points. First, reactions to terrorism can cause additional deaths if relative risks are not taken into account. Cultures also respond to terrorism in different ways, perhaps depending on their previous exposure to violent extremism. Finally, the task of explaining differences is far more difficult than establishing patterns of facts.

Over the weekend I rented Killing them Softly expecting a relatively mindless movie featuring Brad Pitt as a hitman. I was only half right. During several key scenes George W. Bush and his administration officials can be heard giving statements to the press about the financial crisis; Barack Obama is elected near the end. The man hiring Pitt refers to the “corporate mentality” of his bosses and tries to pay him less than he is owed. The plot centers around a gambling racket in New Orleans. Do you get the metaphor yet?

Yes, the movie is a cautionary tale about greed and risk in light of the 2008 financial crisis. The following quotes from an interview with writer and director Andrew Dominik make the connection clear:

[A]s I started adapting it, it was the story of an economic crisis, and it was an economic crisis in an economy that was funded by gambling — and the crisis occurred due to a failure in regulation….

I always feel that crime films are about capitalism, because it’s the one genre where it’s perfectly acceptable for all the characters to be motivated by desire for money only. I always think in some ways the crime film is the most honest American film, because it portrays Americans as I experience them. Particularly in Hollywood, people are very concerned with money….

The film’s not about Obama, it’s about a crisis in the economy, and the people who have to clean it up.

Don’t worry–the movie doesn’t come off as hokey or the metaphor as forced. (The use of politicians as background audio is mostly in the first half.) It’s a satisfying film whether you want the shoot-em-up I expected or something a bit deeper.